Dataset Information

Tweeting back: predicting new cases of back pain with mass social media data.

ABSTRACT:

Background

Back pain is a global health problem. Recent research has shown that risk factors that are proximal to the onset of back pain might be important targets for preventive interventions. Rapid communication through social media might be useful for delivering timely interventions that target proximal risk factors. Identifying individuals who are likely to discuss back pain on Twitter could provide useful information to guide online interventions.

Methods

We used a case-crossover study design for a sample of 742?028 tweets about back pain to quantify the risks associated with a new tweet about back pain.

Results

The odds of tweeting about back pain just after tweeting about selected physical, psychological, and general health factors were 1.83 (95% confidence interval [CI], 1.80-1.85), 1.85 (95% CI: 1.83-1.88), and 1.29 (95% CI, 1.27-1.30), respectively.

Conclusion

These findings give directions for future research that could use social media for innovative public health interventions.

SUBMITTER: Lee H

PROVIDER: S-EPMC7839924 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tweeting back: predicting new cases of back pain with mass social media data.

Lee Hopin H McAuley James H JH Hübscher Markus M Allen Heidi G HG Kamper Steven J SJ Moseley G Lorimer GL

Journal of the American Medical Informatics Association : JAMIA 20151211 3

<h4>Background</h4>Back pain is a global health problem. Recent research has shown that risk factors that are proximal to the onset of back pain might be important targets for preventive interventions. Rapid communication through social media might be useful for delivering timely interventions that target proximal risk factors. Identifying individuals who are likely to discuss back pain on Twitter could provide useful information to guide online interventions.<h4>Methods</h4>We used a case-cross ...[more]

PMID: 26661720

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Project description:Back pain represents a substantial burden globally, ranking first in a recent assessment among causes of years lived with disability. Though back pain is widely studied among working age adults, there are gaps with respect to basic descriptive epidemiology among seniors, especially in the United States. Our goal was to describe how pain, function and health-related quality of life vary by demographic and geographic factors among seniors presenting to primary care providers with new episodes of care for back pain.We examined baseline data from the Back pain Outcomes using Longitudinal Data (BOLD) registry, the largest inception cohort to date of seniors presenting to a primary care provider for back pain. The sample included 5,239 patients ? 65 years old with a new primary care visit for back pain at three integrated health systems (Northern California Kaiser-Permanente, Henry Ford Health System [Detroit], and Harvard Vanguard Medical Associates [Boston]). We examined differences in patient characteristics across healthcare sites and associations of patient sociodemographic and clinical characteristics with baseline patient-reported measures of pain, function, and health-related quality of life.Patients differed across sites in demographic and other characteristics. The Detroit site had more African-American patients (50%) compared with the other sites (7-8%). The Boston site had more college graduates (68%) compared with Detroit (20%). Female sex, lower educational status, African-American race, and older age were associated with worse functional disability as measured by the Roland-Morris Disability Questionnaire. Except for age, these factors were also associated with worse pain.Baseline pain and functional impairment varied substantially with a number of factors in the BOLD cohort. Healthcare site was an important factor. After controlling for healthcare site, lower education, female sex, African-American race, and older age were associated with worse physical disability and all of these factors except age were associated with worse pain.Clinical Trials.gov NCT01776242; Registration date: June 13, 2012.

Dataset Information

Tweeting back: predicting new cases of back pain with mass social media data.

Background

Methods

Results

Conclusion

Publications

Tweeting back: predicting new cases of back pain with mass social media data.

Similar Datasets

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